Integrating Implicit and Explicit Relational Biases through Graph-Based Multiple Instance Learning: A Case Study in Skin Lesion Diagnosis

• URL: http://arxiv.org/abs/2608.06037v1
• Date: Thu, 06 Aug 2026 13:47:18 GMT
• Title: Integrating Implicit and Explicit Relational Biases through Graph-Based Multiple Instance Learning: A Case Study in Skin Lesion Diagnosis
• Abstract summary: This study investigates a dual-level framework for image classification.<n>It bridges the gap between implicit representation learning and explicit structural modelling.<n> Experimental results show that combining implicit interpatch modelling with explicit graph-based messages yields the best results.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Relational inductive biases are essential for capturing structural dependencies among data. This study investigates a dual-level relational framework for image classification, bridging the gap between implicit representation learning and explicit structural modelling. We begin by establishing a baseline using an EfficientNetB3 architecture. To move beyond standard convolutional biases, we adopt a patch-based strategy, employing a convolutional masked autoencoder to learn implicit inter-patch relationships through self-supervised reconstruction. We then extend this approach by incorporating explicit relational modelling, organizing the learned embeddings into various graph topologies, including grid-based, random, and k-nearest neighbour structures. Experimental results on the ISIC-2018 and ISIC-2019 skin lesion diagnosis benchmarks show that combining implicit inter-patch modelling with explicit graph-based message passing yields the best performance. On the ISIC-2018 test set, the baseline model achieves a balanced accuracy of 76.17%, which improves to 77.12% with implicit patch-based relational modelling. The fully integrated grid-structured Graph Attention Network further increases performance to 79.27%. Similarly, on ISIC-2019, the implicit approach reaches 59.84% balanced accuracy, while the combination of implicit and explicit modelling yields 60.67%.

Related papers

• Text-Guided Visual Dependency Graph Learning with Cross-Modal Attention Priors [11.338098263634537]
  We propose CM-GLasso, a framework that bridges vision-language representation learning and sparse Gaussian Models.<n> CM-GLasso introduces three key components: (i) a text visualization strategy that renders class-attribute descriptions as images and processes them through the same SigLIP-2 vision encoder as natural images, (ii) a cross-attention distillation mechanism that condenses high-dimensional patches into a small set of semantic graph nodes, and (iii) a joint ADMM formulation that estimates shared and class-specific precision components within a single convex objective.
  arXiv  Detail & Related papers  (2026-08-19T09:57:34Z)
• HGT-Scheduler: Deep Reinforcement Learning for the Job Shop Scheduling Problem via Heterogeneous Graph Transformers [0.0]
  Job Shop Scheduling Problem (JSSP) is commonly formulated as a disjunctive graph in which nodes represent operations and edges encode technological precedence constraints as well as machine-sharing conflicts.<n>Most existing reinforcement learning approaches model this graph as homogeneous, merging job-precedence and machine-contention edges into a single relation type.<n>We propose the Heterogeneous Graph Transformer (HGT)-Scheduler, a reinforcement learning framework that models the JSSP as a heterogeneous graph.
  arXiv  Detail & Related papers  (2026-03-06T17:59:12Z)
• Concept Factorization via Self-Representation and Adaptive Graph Structure Learning [8.990462532663871]
  We propose a Concept Factorization Based on Self-Representation and Adaptive Graph Structure Learning (CFSRAG) Model.<n>CFSRAG learns the affinity relationship between data through a self-representation method, and uses the learned affinity matrix to implement dynamic graph regularization constraints.<n>The results show that our model outperforms other state-of-the-art models.
  arXiv  Detail & Related papers  (2025-05-06T10:12:59Z)
• Graph Structure Refinement with Energy-based Contrastive Learning [56.957793274727514]
  We introduce an unsupervised method based on a joint of generative training and discriminative training to learn graph structure and representation.<n>We propose an Energy-based Contrastive Learning (ECL) guided Graph Structure Refinement (GSR) framework, denoted as ECL-GSR.<n>ECL-GSR achieves faster training with fewer samples and memories against the leading baseline, highlighting its simplicity and efficiency in downstream tasks.
  arXiv  Detail & Related papers  (2024-12-20T04:05:09Z)
• A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning [49.09125100268454]
  Knowledge Graph Embedding (KGE) has proven to be an effective approach to solving the Knowledge Completion Graph (KGC) task. Relational patterns are an important factor in the performance of KGE models. We introduce a training-free method to enhance KGE models' performance over various relational patterns.
  arXiv  Detail & Related papers  (2023-08-15T17:30:57Z)
• Homophily-enhanced Structure Learning for Graph Clustering [19.586401211161846]
  Graph structure learning allows refining the input graph by adding missing links and removing spurious connections. Previous endeavors in graph structure learning have predominantly centered around supervised settings. We propose a novel method called textbfhomophily-enhanced structure textbflearning for graph clustering (HoLe)
  arXiv  Detail & Related papers  (2023-08-10T02:53:30Z)
• Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning [78.49090351193269]
  We propose a novel graph-based framework to leverage the inter-relationships among different types of nuclei for WSI analysis. Specifically, we formulate the WSI as a heterogeneous graph with "nucleus-type" attribute to each node and a semantic attribute similarity to each edge. Our framework outperforms the state-of-the-art methods with considerable margins on various tasks.
  arXiv  Detail & Related papers  (2023-07-09T14:43:40Z)
• GraphLearner: Graph Node Clustering with Fully Learnable Augmentation [76.63963385662426]
  Contrastive deep graph clustering (CDGC) leverages the power of contrastive learning to group nodes into different clusters. We propose a Graph Node Clustering with Fully Learnable Augmentation, termed GraphLearner. It introduces learnable augmentors to generate high-quality and task-specific augmented samples for CDGC.
  arXiv  Detail & Related papers  (2022-12-07T10:19:39Z)
• Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing [52.24507547010127]
  Cross-domain context-dependent semantic parsing is a new focus of research. We present a dynamic graph framework that effectively modelling contextual utterances, tokens, database schemas, and their complicated interaction as the conversation proceeds. The proposed framework outperforms all existing models by large margins, achieving new state-of-the-art performance on two large-scale benchmarks.
  arXiv  Detail & Related papers  (2021-01-05T18:11:29Z)
• Amortized Probabilistic Detection of Communities in Graphs [39.56798207634738]
  We propose a simple framework for amortized community detection. We combine the expressive power of GNNs with recent methods for amortized clustering. We evaluate several models from our framework on synthetic and real datasets.
  arXiv  Detail & Related papers  (2020-10-29T16:18:48Z)
• Out-of-distribution Generalization via Partial Feature Decorrelation [72.96261704851683]
  We present a novel Partial Feature Decorrelation Learning (PFDL) algorithm, which jointly optimize a feature decomposition network and the target image classification model. The experiments on real-world datasets demonstrate that our method can improve the backbone model's accuracy on OOD image classification datasets.
  arXiv  Detail & Related papers  (2020-07-30T05:48:48Z)
• Tensor Graph Convolutional Networks for Multi-relational and Robust Learning [74.05478502080658]
  This paper introduces a tensor-graph convolutional network (TGCN) for scalable semi-supervised learning (SSL) from data associated with a collection of graphs, that are represented by a tensor. The proposed architecture achieves markedly improved performance relative to standard GCNs, copes with state-of-the-art adversarial attacks, and leads to remarkable SSL performance over protein-to-protein interaction networks.
  arXiv  Detail & Related papers  (2020-03-15T02:33:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.